BindsNET丨环境配置及安装

Posted 2023-03-10 AXYZdong

环境说明

操作系统：Windows 10

CUDA 版本为: 10.0

cudnn 版本为: 7.6.5

Python 版本为：Python 3.8

PyTorch 版本为：1.8.1

IDE：PyCharm

注意CUDA、cudnn、Python、PyTorch版本之间的匹配

BindsNET项目地址：https://github.com/BindsNET/bindsnet

官方推荐安装过程

官方推荐安装如下：

我的安装过程

• 第一步

使用anaconda创建一个python3.8的环境

• 第二步

安装pytorch

根据自己的电脑配置，在刚刚创建的的环境下，安装Pytorch。

Anaconda Prompt下输入以下指令激活环境，其中***为你创建环境的名称。

activate ***

转到创建的环境后，输入指令安装Pytorch。我的安装指令如下：

conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.2 -c pytorch

根据自己的电脑配置选择性进行安装，Pytotch官网。

• 第三步

安装可视化包 matplotlib

pip install matplotlib

• 第四步

测试代码，代码地址：https://bindsnet-docs.readthedocs.io/guide/guide_part_i.html#creating-a-network

代码运行成功就OK了。

# =====-*- coding: utf-8 -*-=====
# @Time  : 2023/3/9 17:02
# @Author: AXYZdong
# @File  : demo.py
# @IDE   : Pycharm
# ===============================
import torch
import matplotlib.pyplot as plt
from bindsnet.network import Network
from bindsnet.network.nodes import Input, LIFNodes
from bindsnet.network.topology import Connection
from bindsnet.network.monitors import Monitor
from bindsnet.analysis.plotting import plot_spikes, plot_voltages

# Simulation time.
time = 500

# Create the network.
network = Network()

# Create and add input, output layers.
source_layer = Input(n=100)
target_layer = LIFNodes(n=1000)

network.add_layer(
    layer=source_layer, name="A"
)
network.add_layer(
    layer=target_layer, name="B"
)

# Create connection between input and output layers.
forward_connection = Connection(
    source=source_layer,
    target=target_layer,
    w=0.05 + 0.1 * torch.randn(source_layer.n, target_layer.n),  # Normal(0.05, 0.01) weights.
)

network.add_connection(
    connection=forward_connection, source="A", target="B"
)

# Create recurrent connection in output layer.
recurrent_connection = Connection(
    source=target_layer,
    target=target_layer,
    w=0.025 * (torch.eye(target_layer.n) - 1), # Small, inhibitory "competitive" weights.
)

network.add_connection(
    connection=recurrent_connection, source="B", target="B"
)

# Create and add input and output layer monitors.
source_monitor = Monitor(
    obj=source_layer,
    state_vars=("s",),  # Record spikes and voltages.
    time=time,  # Length of simulation (if known ahead of time).
)
target_monitor = Monitor(
    obj=target_layer,
    state_vars=("s", "v"),  # Record spikes and voltages.
    time=time,  # Length of simulation (if known ahead of time).
)

network.add_monitor(monitor=source_monitor, name="A")
network.add_monitor(monitor=target_monitor, name="B")

# Create input spike data, where each spike is distributed according to Bernoulli(0.1).
input_data = torch.bernoulli(0.1 * torch.ones(time, source_layer.n)).byte()
inputs = "A": input_data

# Simulate network on input data.
network.run(inputs=inputs, time=time)

# Retrieve and plot simulation spike, voltage data from monitors.
spikes = 
    "A": source_monitor.get("s"), "B": target_monitor.get("s")

voltages = "B": target_monitor.get("v")

plt.ioff()
plot_spikes(spikes)
plot_voltages(voltages, plot_type="line")
plt.show()

—— END ——

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如果以上内容有任何错误或者不准确的地方，欢迎在下面 👇 留言。或者你有更好的想法，欢迎一起交流学习~~~

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